Infrastructure-free rf tracking in dynamic indoor environments

ABSTRACT

Aspects of the present disclosure describe systems, methods, and structures infrastructure-free RF tracking in dynamic indoor environments.

CROSS REFERENCE

This disclosure claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/946,657 filed Dec. 11, 2019, and U.S.Provisional Patent Application Ser. No. 62/947,781 filed Dec. 13, 2019,the entire contents of each incorporated by reference as if set forth atlength herein.

TECHNICAL FIELD

This disclosure relates generally to tracking mobile entities indoors.More particularly, it pertains to RF tracking of mobile entities in aninfrastructure-free, dynamic indoor environment.

BACKGROUND

As is known, promising solutions exist today that can accurately trackmobile entities indoors using visual inertial odometry in favorablevisual conditions, or by leveraging fine-grained ranging (RF,ultrasonic, IR, etc.) to reference anchors. However, such solutions areunable to effectively operate in “dynamic” indoor environments (e.g.first responder scenarios, multi-player AR/VR gaming in everyday spaces,etc.) that are devoid of such favorable conditions or reference anchors.

SUMMARY

The above problem is solved and an advance in the art is made accordingto aspects of the present disclosure directed to systems, methods, andstructures that provide the locating/tracking of mobile entities in adynamic indoor environment without infrastructure support or—as notedabove—favorable visual conditions.

In sharp contrast to the prior art, and viewed from a firstaspect—systems, methods, and structures according to aspects of thepresent disclosure provide for the infrastructure-free tracking by:providing each individual one of the plurality of nodes located in theinfrastructure-free, dynamic indoor environment with a radio tag;determining a topology estimate of the plurality of nodes; determiningrelative localizations of the plurality of nodes by generating rigidk-core sub-graphs of the topology estimate; transforming the relativelocalizations into absolute localizations; and outputting indicia of theabsolute locations.

BRIEF DESCRIPTION OF THE DRAWING

A more complete understanding of the present disclosure may be realizedby reference to the accompanying drawing in which:

FIG. 1(A) is a schematic diagram showing an illustrative, simpleexperiment to demonstrate persistent localization errors in a VIO-basedsystem;

FIG. 1(B) is a plot showing localization error for various lightingconditions for the experiment using a VIO-based system;

FIG. 2 is a schematic diagram showing illustratively personnel withDynoLoc tags, the DynoLoc system overview, and a fire chief monitoring aDynoLoc dashboard according to aspects of the present disclosure;

FIG. 3(A) is a plot showing localization accuracy as a function ofdependence on deployed infrastructure according to aspects of thepresent disclosure;

FIG. 3(B) is a plot showing localization accuracy as a function of thedynamics of environment according to aspects of the present disclosure;

FIG. 4(A) is a weighted graph illustrating a Clique (rigid) arrangementaccording to aspects of the present disclosure;

FIG. 4(B) is a weighted graph illustrating a non-clique (rigid)arrangement according to aspects of the present disclosure;

FIG. 4(C) is a weighted graph illustrating two choices for node 5 (notrigid) arrangement according to aspects of the present disclosure;

FIG. 5(A) is a plot showing localization error for various refresh ratesaccording to aspects of the present disclosure;

FIG. 5(B) is a plot showing localization error for various number ofnodes according to aspects of the present disclosure;

FIG. 6(A) is a plot showing localization error after using variouslow-rank matrix completion techniques for various topologies accordingto aspects of the present disclosure;

FIG. 6(B) is a plot showing localization error after using variouslow-rank matrix completion techniques for various set of edges from sametopology according to aspects of the present disclosure;

FIG. 7(A) is a plot showing localization error for various levels oflink noises according to aspects of the present disclosure;

FIG. 7(B) is a plot showing localization error for various fractions ofmobile nodes according to aspects of the present disclosure;

FIG. 7(C) is a block-flow diagram showing a schematic overview of theoverall DynoLoc operation.

FIG. 8 is a plot showing first path detection in UWB receiver accordingto aspects of the present disclosure;

FIG. 9 is a schematic diagram illustrating an example of k-coredecomposition according to aspects of the present disclosure;

FIG. 10 is a schematic diagram illustrating a prototype of DynoLoc nodeaccording to aspects of the present disclosure;

FIG. 11(A), FIG. 11(B), FIG. 11(C), and FIG. 11(D), are a series ofplots showing comparison of localization error for various method oflocalization in which: FIG. 11(A) is No. of nodes; FIG. 11(B) is %Mobile Nodes; FIG. 11(C) is Refresh Rate (Hz); and FIG. 11(D) isVelocity (m/s), according to aspects of the present disclosure;

FIG. 12(A) is a plot showing consistency of Mobility Metric according toaspects of the present disclosure;

FIG. 12(B) is a plot showing correlation of Link Quality metric withranging error according to aspects of the present disclosure;

FIG. 13(A) is a plot showing comparison of DynaLoc without MobilityMetric according to aspects of the present disclosure;

FIG. 13(B) is a plot showing comparison of DynaLoc without Link Quality(L) metric according to aspects of the present disclosure;

FIG. 14(A) is a plot showing EDM completion error according to aspectsof the present disclosure;

FIG. 14(B) is a plot showing relative location error according toaspects of the present disclosure;

FIG. 15(A) is a plot showing ranging error for different modes of UWBaccording to aspects of the present disclosure;

FIG. 15(B) is a plot showing location accuracy & node dis-connectivityfor concurrent ranging UWB data rates and node densities according toaspects of the present disclosure;

FIG. 16(A) is a plot showing accuracy of absolute localization accordingto aspects of the present disclosure;

FIG. 16(B) is a plot showing impact of heading error according toaspects of the present disclosure;

FIG. 17(A) is a plot showing breakdown of latency components accordingto aspects of the present disclosure;

FIG. 17(B) shows power benchmarks for various system components andadaption to an unknown environment according to aspects of the presentdisclosure;

FIG. 18(A) is an illustration of DynoLoc enabled location-basedmultiplayer gaming according to aspects of the present disclosure;

FIG. 18(B) is a plot showing mobility-aware range scheduling accordingto aspects of the present disclosure;

FIG. 18(C) illustrates mobility-aware range scheduling distributed timeslots across nodes improving overall interactivity of a game accordingto aspects of the present disclosure;

FIG. 19(A) illustrates a DynoLoc enabled real time geofencing accordingto aspects of the present disclosure; and

FIG. 19(B) is a plot showing the illustration of FIG. 19(A) according toaspects of the present disclosure.

The illustrative embodiments are described more fully by the Figures anddetailed description. Embodiments according to this disclosure may,however, be embodied in various forms and are not limited to specific orillustrative embodiments described in the drawing and detaileddescription.

DESCRIPTION

The following merely illustrates the principles of the disclosure. Itwill thus be appreciated that those skilled in the art will be able todevise various arrangements which, although not explicitly described orshown herein, embody the principles of the disclosure and are includedwithin its spirit and scope.

Furthermore, all examples and conditional language recited herein areintended to be only for pedagogical purposes to aid the reader inunderstanding the principles of the disclosure and the conceptscontributed by the inventor(s) to furthering the art and are to beconstrued as being without limitation to such specifically recitedexamples and conditions.

Moreover, all statements herein reciting principles, aspects, andembodiments of the disclosure, as well as specific examples thereof, areintended to encompass both structural and functional equivalentsthereof. Additionally, it is intended that such equivalents include bothcurrently known equivalents as well as equivalents developed in thefuture, i.e., any elements developed that perform the same function,regardless of structure.

Thus, for example, it will be appreciated by those skilled in the artthat any block diagrams herein represent conceptual views ofillustrative circuitry embodying the principles of the disclosure.

Unless otherwise explicitly specified herein, the FIGs comprising thedrawing are not drawn to scale.

By way of some additional background, we note that there existcontemporary solutions that can accurately track mobile entities indoorsusing visual inertial odometry in favorable visual conditions, or byleveraging fine-grained ranging (RF, ultrasonic, IR, etc.) to referenceanchors. However, they are unable to directly cater to “dynamic” indoorenvironments (e.g. first responder scenarios, multi-player AR/VR gamingin everyday spaces, etc.) that are devoid of such favorable conditions.As such, there exists a continuing and pressing need for systems,methods, and structures providing mobile entity tracking in an“infrastructure-free” environment, while providing a robustness to “nodemobility” and “visual conditions” in such environments. Such systems,methods and structures must also address a novel and challenging variantof infrastructure-free (i.e. peer-to-peer) localization problems thatare latency-bounded—wherein accurate tracking of mobile entities imposesa latency budget that not only affects solution computation but also thecollection of peer-to-peer ranges themselves.

As we shall show and describe, we present a design and illustrativedeployment of a system we call “DynoLoc”, that advantageously addressesthis latency-bounded infrastructure-free RF localization problem. Tothis end, DynoLoc unravels the fundamental tradeoff between latency andlocalization accuracy and incorporates design elements that judiciouslyleverage the available ranging resources to adaptively estimate thejoint topology of nodes, coupled with robust algorithm(s) that maximizeslocalization accuracy even in the face of practical environmentalartifacts (wireless connectivity and multipath, node mobility, etc.).This advantageously allows DynoLoc to track (every second) a network oftens of mobile entities even at speeds of 1-2 m/s with median accuraciesunder 1-2m (compared to 5 m+ with baselines)—without infrastructuresupport. We illustratively demonstrate DynoLoc's potential in areal-world firefighter drill, as well as two other use cases of (i)multi-player AR/VR gaming, and (ii) active shooter response/tracking byfirst responders.

INTRODUCTION

Dynamic indoor environments. As we have note previously, there existseveral promising solutions for indoor localization that leveragevarious modalities (RF, ultrasonic, optical (IR) tracking, etc.) andmultiple dimensions (antennas, channels, access points, etc.) to providefine-grained (sub-meter, decimeter-level) localization.

As noted further, the effectiveness of such contemporary solutions isextremely dependent on the particular environment in which they operate.Note that the majority of contemporary localization solutions requirestatic anchor nodes positioned in in known locations to provide distanceestimate(s) to a target client (a.k.a. ranging), which are thenaggregated to determine its location.

Infra-free solutions—on the other hand—that employ inertial sensors areprone to accumulating errors of tens of meters over extended periods oftime. Even high-accuracy (cm-level) solutions for AR/VR that are basedon Visual Inertial Odometry (VIO) such as fuse cameras and IMUs, sufferappreciably (as shown schematically in FIG. 1(A) and FIG. 1(B)) when theoperating environment is not well lit/textured, has motion blur and/ordynamic entities in screen, etc. Unfortunately, and as will be readilyunderstood and appreciated by those skilled in the art, existinganchor-based and infra-free solutions do not effectively operate in“dynamic” indoor environments that are inherently characterized by thelack of reference anchors, unfavorable visual conditions, and mobileentities.

RF-based localization for dynamic environments: Localization andtracking in such dynamic environments, especially with mobile clients,are not only central to all first responder scenarios, but also enablenew capabilities in emerging consumer applications including mixedreality (MR) gaming—where multiple players engaged in an AR/VR game aretracked in real-time across large, everyday (unmapped) indoor spacesthat span multiple rooms in less-than-favorable visual conditions. Thoseskilled in the art will appreciate that an RF-based localizationsolution that can deliver high accuracies (sub-meter, i.e., cm)—withoutrelying on anchors—can fill this critical need as a stand-alone solutionfor first responder applications, and a complementary solution to VIO(for alleviating its errors) for MR gaming applications, owing to itsrobustness in unfavorable visual conditions.

Gap between ranging and localization. Obtaining accurate ranges (i.e.distances) between clients (nodes) and anchors in infrastructureapplications, automatically leads to accurate localization of theclients through multilateration. However, in the absence of such anchorinfrastructure, nodes only range with respect to each other. Of course,and as will be readily appreciated by those skilled in the art, there isa large technical gap between such relative ranging and localization indynamic environments, where existing multilateration approaches cannotbe effectively employed.

Challenges in addressing the gap. As will be appreciated, localizingnodes in an absolute frame of reference is challenging without one ormore reference nodes. However, existing works in the sensor literaturehave shown that if one can estimate the relative geometry of nodes(called relative localization) using their pair-wise measured ranges;then, additional information (such as IMU data, floor plan, etc.) can beused to potentially rotate, translate or flip this relative geometry toobtain the absolute localization of the nodes. The resulting effortsthat focused on relative localization albeit amenable to theoreticalanalysis, however, do not account for a critical dimension needed forpractical deployments, namely node mobility. Incorporating node mobilityhowever, significantly changes the nature of the problem, requiring oneto solve the latency-bounded version of the infrastructure-free RFlocalization problem, which has heretofore not been addressed. Indeed,infrastructure free localization solutions today are unable to trackeven a network of around 10 mobile nodes (at just 1 m/s speed) with anaccuracy of under 6 m. This can be attributed to the following keychallenges:

(i) Latency vs. accuracy tradeoff: Accurate location determination ofthe nodes requires that nodes be tracked at least once every second(i.e. refresh rate of 1 Hz) for a node mobility of 1-1.5 m/s. Thecorresponding latency constraint restricts the number of node-pairs thatcan be ranged (before computing a localization solution), therebylowering the accuracy of localization significantly by several folds.Further, the quality of links (edges) and hence the ranges measured, arein turn impacted by the geometry of the induced topology, node mobilityas well as the multi-path wireless channel, and have a large impact onthe accuracy as well.

(ii) Overhead of range measurements: Ranging between node pairs isaccomplished through sequential packet exchanges and time-of-flightestimation techniques , thereby incurring a large latency and hencereduced ability to track a large network of mobile nodes.

(iii) Partial information degrades accuracy: Existing solutions forrelative localization (e.g. techniques using Euclidean distancematrices, EDM) work well when network topologies are a complete graphand all range estimates are available and accurate. However, in theabsence of such features in practical deployments, the accuracy cansuffer appreciably.

DynoLoc (Dynamic Indoor Localization) design. Towards addressing thesechallenges, we present DynoLoc—a system and attendant methods andstructure for latency-bounded infrastructure-free localization thatadvantageously may be readily deployed in dynamic indoor environments.While DynoLoc's framework is agnostic to the underlying wirelesstechnology (e.g. WiFi, UWB, mmWave) used for ranging, it currentlyemploys UWB, given the latter's ability to offer a desirable rangingresolution (tens of cm) at reasonable indoor penetration (70-90 m LOS,30-50 m NLOS).

As we shall show and describe further, DynoLoc equips each of node thatrequires tracking with a tag that utilizes a UWB radio (for ranging),WiFi radio (for control/orchestration), and IMU. While UWBs are theprimary source of active ranging, IMUs are used in a limited scope(heading and mobility indication) to resolve ambiguities inlocalization. DynoLoc's design involves three key components:

Topology estimation for ranging: DynoLoc intelligently uses itsavailable ranging resources on critical links that will contribute themost to topology's localization accuracy. The critical nature of a linkvaries spatio-temporally and is determined by DynoLoc by fusing threedimensions of information, namely (a) mobility of nodes in the link(that affect the staleness of its range measurement), (b) certainty ofrange estimates being LOS vs. NLOS (inferred from channel impulseresponse measurements), and (c) link's contribution to the topology'sgeometry in creating a robust and maximally rigid (where relativelocation of nodes are fixed in the topology) sub-graph that in turnleads to increased localization accuracy.

Aggregated and concurrent ranging: DynoLoc redesigns the traditionalpair-wise and sequential ranging protocol for reduced measurementlatency. It aggregates (and amortizes), the process of ranging (andassociated overhead) for a node with all its neighbors into a singlecompacted process, while links that are spatially separated, can enablesuch ranging concurrently.

Robust relative localization: Instead of applying EDM completiontechniques on the entire topology that is incomplete, DynoLoc leveragesthe graph rigidity construct of k-core sub-graphs to identify maximalrigid sub-graphs of the topology, and applies EDM on these separatelyand combines them to provide a robust, accurate solution. Given therelative localization of nodes in the rigid sub-graphs, DynoLoc devisesadditional mechanisms to localize the remaining nodes in the topology byleveraging geometric constraints driven by range, physical connectivityas well as heading data from IMUs.

Finally, with little additional meta information, contributed by IMUheading data or floor plans, DynoLoc efficiently transforms the relativelocalization solution into absolute coordinate system without affectingthe solution's refresh rate.

As we shall show and describe, we have built and deployed DynoLoc inreal-world dynamic environments, including in a live firefighters' drill(see, FIG. 2), where its accuracy and value in saving lives waswell-appreciated. Designed with mobility (and hence latency) in mind,DynoLoc delivers superior performance in infra-free tracking acrossmultiple dimensions of node density, mobility, application refresh rate,etc.

In particular, evaluation in two real-world use-cases, reveal that (i)first-responder scenarios: DynoLoc is able to track a network of 12 (20)responders, operating with speeds up to 1 m/s (2 m/s) with a medianlocalization error of under 1 m (2 m), while delivering a refresh rateof 1 Hz; existing solutions suffer in accuracy (6 m+error) even for anode mobility of 1 m/s; (ii) AR gaming: DynoLoc tracks translationalmotion of users accurately across a free-flowing indoor space of 20 m×20m with latencies of under 64 msec to enable a highly-responsive, dynamic3-player AR game even in sub-optimal lighting conditions—a scenario thatis challenging for VIO, especially in the multi-player context. Infuture, we aim to fuse VIO with DynoLoc to deliver on VIO's performanceeven in realistic everyday scenarios.

As we shall show and describe, we introduce and address a problem oflatency bounded, infrastructure free localization that is critical fortracking in several dynamic indoor applications. Toward that end, webuild and demonstrate the viability of such a system we call DynoLoc, intwo real world use-cases targeting first responders (a realfirefighters' drill and active shooter tracking) and multi-player AR/VRgaming.

We consider the problem of tracking mobile entities (nodes) in dynamicindoor environments, namely those that are un-calibrated, lackexternally deployed localization infrastructure, and characterized bynode mobility. This features a pressing need in first respondersituations (as evident from NIST programs), with the potential to enableanchor-less user tracking in multiplayer AR/VR gaming applications inthe future.

Additional Background on Related Approaches

We note that the literature is rich in the area of active (locating andidentifying) indoor localization which can be broadly categorized as (i)Anchor-based, and (ii) Infrastructure-free, as shown in FIG. 3.

Anchor-based. Anchor-based approaches often surpass their infra-freecounterparts in accuracy at the expense of apriori deployedinfrastructure for localization—a tradeoff captured in FIG. 3. Here,beacons are deployed at known locations and serve as reference points oranchors. A node estimates its distances (also called ranges) from threesuch anchors, which are then combined with the anchors' locations toestimate its own location by a technique typically known in the art asmultilateration. Given a technology to perform accurate ranging,localization can be considered a trivial extension. Hence, most of theprior art in this space has focused on the accurate estimation of suchranges, particularly using WiFi access points as beacons, while somehave also leveraged ultrasonic beacons.

WiFi-based approaches leverage signal information across multipledimensions—frequency, antenna arrays, or both, to improve accuracy inthe face of limited WiFi bandwidth and multipath. Some of them adopt afinger-printing approach (using RSSI, CSI, etc.) to calibrate theenvironment a-priori that is later used for real-time locationinference.

Optical tracking systems (e.g. HTC Vive) that are popular in the AR/VRindustry, employ multiple IR beacons (LEDs/cameras) to provide mm-leveltracking accuracy, but are restricted to line-of-sight and expensive todeploy.

Those skilled in the art will—at this point—recognize that thefundamental dependence on pre-deployed anchors (mostly static, butsometimes mobile—e.g. outdoor drones), prevents such prior artapproaches from successful deployment in our noted target environment.

Infrastructure-free. Infrastructure-free approaches are more amenable toour target environment, but exhibit a different tradeoff betweenaccuracy and robustness, as captured in FIG. 3. One particularinfrastructure-free approach, inertial sensor-based, are inherentlylocal to a node (no ranging needed), and hence popular. However, withonly dead-reckoning of nodes, errors accumulate significantly over time,especially in case of pedestrian mobility.

State-of-the art AR/VR solutions (e.g. ARCore) leverage visual inertialodometry (VIO) that combines both cameras and IMUs to provide accuratecm-level tracking in favorable conditions. However, such AR/VRapproaches require anchors and their performance suffers significantlyin poorly illuminated and/or poorly textured environments, in presenceof motion-blur and/or multiple moving objects in the video-frames, asshown in FIG. 1. Such practical conditions result in various errorsrelating to drift, loop-closure, scale ambiguity etc. (for SLAM-basedapproaches), and errors related to projection, parameterization etc.(for optical-flow based approaches).

Those skilled in the art will readily understand and appreciate thatdynamic environments—particularly those in first responder scenarios—cansignificantly benefit from an alternate RF modality that can delivergood accuracies (sub-1-2 m), and which is robust to the lacking of suchfavorable conditions. In AR/VR gaming applications, such a modality canbe complementary in helping to eliminate the accumulating errors facedby VIO, with periodic absolute location fixes.

Role of RF in Infra-free Localization

The recent popularity of ultra wide-band (UWB) technology, and itsability to span a wide 500-1000 MHz bandwidth with superior multipathsuppression (owing to its impulse transmissions), has made it a popularcandidate for sub-m localization, albeit with the help of infrastructureanchors. Existing works in this space are largely concerned withscalable ranging (SurePoint, SnapLoc) and tracking of individual mobilenodes (e.g., indoor drone, PolyPoint). However, in the absence ofreference anchors, UWB's two-way-ranging (TWR) mechanism can enable thenodes to only range with each other. Hence, localization in dynamicenvironments presents a different challenge, which, beyond theestimation of accurate ranges, needs to translate the ranges to anaccurate localization solution. Indeed, the key focus of infra-free RFlocalization comes down to bridging this gap between estimated rangesand node localization that arises in the absence of anchors.

Primer on Relative Localization. In contrast to anchor-based approaches,where nodes are absolutely localized in the coordinate space defined bythe anchors, localization in infra-free set-up is a two-step process.First, the nodes are relatively localized among themselves, followingwhich some meta information (e.g. orientation of the nodes, orfloorplans) is leveraged to transform such relative localization to theabsolute coordinate space. Relative localization refers to the geometryor a topology among the nodes, where the pairwise distance between nodesas well as their relative orientation are preserved. In determining sucha relative localization, the construct of a rigid body is useful.

Let G=(V,E,d) be a weighted graph, where d is the set of rangemeasurements (weights) for the |E| edges defined on the |V| nodes. Arealization is a function x: V→R² that maps the set of vertices V to the2D Euclidean space such that each range value is preserved i.e ∀(u,v)∈E,∥x(u)−x(v)∥=d(u, v) where ∥.∥ is the Euclidean norm.

The graph G=(V,E,d) is “rigid” if there is only one realization,discounting any translation, rotation and flip. Thus, a rigid topologygives us a unique relative localization solution. As shown in FIG. 4(A),FIG. 4(B), and FIG. 4(C), the complete graph or clique in FIG. 4(A) isrigid because given the 10 ranges between all pairs of nodes, this graphis the only realization, although it can be rotated, translated and/orflipped. Similarly, given the 9 ranges, the graph in FIG. 4(B) is rigid.However, that shown in FIG. 4(C) is not rigid, since given the 8 ranges,there are two choices to fix node 5 relative to the edge (2,3) resultingin two potential realizations.

If all possible ranges between nodes are available, computing therelative localization is straight-forward in a static environment. Theedge weights of the graph are maintained in the form of an adjacencymatrix, EDM (a.k.a. Euclidean Distance Matrix), where each entryrepresents a measured range between two nodes. Then, an approach calledMultidimensional Scaling (MDS) is applied on the EDM matrix, whereby anEVD (EigenValue Decomposition) results in an embedding of the nodes(i..e. relative localization) in a 2D Cartesian space. While such aframework of relative localization is appropriate for our dynamicenvironments, the theoretical approaches in this space are built onseveral assumptions¹ that do not hold in practice. ¹ e.g. static nodes,accurate range estimates of all node pairs at no cost, etc.

Additional Challenges in a Dynamic Environment

To accurately localize/track mobile nodes, the localization solutionneeds to be computed and refreshed at a granularity finer than nodemobility. For instance, a 1 Hz refresh rate is appropriate to tracknodes with speeds of 1-1.5 m/s, targeting a 1-2 m error. However, therefresh rate automatically enforces a latency-bound (cost) for the wholeprocess of relative localization, which involves both the rangeestimation/collection as well as the solution computation. This resultsin a latency bounded version of the infra-free localization problem(referred to as LB-IFL) that has not been addressed before.

To understand the impact of such a latency cost on existing approachesin practice, we show an describe an experimental study comparing agenie/anchor-aided localization solution (with all possible rangeestimates available instantaneously, TDOA) with the one described above,i.e., MDS applied on EDM constructed with a random set of edges (calledBase), whose ranges are measured within the latency budget offered bythe refresh rate.

Latency vs. accuracy. Those skilled in the art will readily understandand appreciate that every ranging operation takes a finite amount oftime to complete. For instance, with a popular UWB hardware, it takesapproximately 40 ms to complete a single range estimate, i.e. atwo-way-ranging (TWR) operation. This inherently limits the number ofranges that can be estimated/collected per second to 25 to support alocalization update rate of 1 Hz. The results in FIG. 5(A) and FIG. 5(B)show that when budget restrictions increase for a given topology size orvice versa, the accuracy degrades by several folds, clearly exposing atrade-off between latency (cost) and accuracy. This can be attributed tothe lack of intelligent topology estimation (edge selection) and robustrelative localization schemes that are needed to work with limitedlatency budgets and hence incomplete range estimatesrespectively—aspects that have not been addressed thus far. We nowfurther dissect the specific factors contributing to this performancedegradation.

Incomplete range estimates. In practice, the physical communicationrange between nodes will limit the topology from being complete. This isfurther compounded by the limited number of edges (ranges) that can beestimated due to the latency budget. Since an incomplete EDM (i.e.estimated topology is not a clique) can lead to localizationinaccuracies, matrix completion methods (e.g., SDR, OptSpace) are usedto complete the EDM before the nodes can be relatively localized.However, the latter are not designed keeping in mind the geometricalimplications relevant to a localization problem. This can lead to largelocalization errors as shown in FIG. 6(A), thereby advocating the needfor relative localization schemes that are robust to incomplete rangeestimates.

Impact of geometry. An interesting result shown in FIG. 6(B) furtherindicates that the specific set of edges selected, albeit incomplete,has a large impact on the localization solution as well. This indicatesthat the geometry of the topology (particularly its rigidity) associatedwith the edges measured, has a direct impact on the solution and must befactored into the edge and hence topology selection process.

Inaccurate range estimates. Inaccurate estimates of even a small set ofranges can lead to degraded accuracy for the entire topology. Here, twokey environmental factors, namely LOS blockages (due to body, concrete,etc.), and node mobility, can significantly affect the accuracy of therange estimates. Given the limited budget for range estimation, it isclear that when the edges are picked randomly without taking intoaccount their channel or mobility characteristics, the performancedegrades quite rapidly even with a small set of affected edges, as seenin FIG. 7(A) and FIG. 7(B). Thus, characterizing the nature of the edgeswith respect to their channel and mobility is essential for improvedlocalization accuracy.

Design Requirements

From the above discussions, we now note that the combination of“infrastructure-free” and “node mobility” in practical, dynamic indoorenvironments, makes the latency bounded version of the localizationproblem, highly challenging.

In addressing these challenges, two key design requirements are pursuedfor DynoLoc namely, (a) Support a reasonable number of nodes in apractical deployment setting few 10 s), many of them being mobile (≤2m/s), and (b) Offer a location update rate (≥1Hz), tolerable to theunderlying location based service that eventually consumes suchinformation.

DynoLoc Design

FIG. 7(C) is a block-flow diagram showing a schematic overview of theoverall DynoLoc operation.

At a high level, DynoLoc models the topology among nodes collectively asa graph of rigid components and tracks it accurately over time as thetopology evolves subject to node mobility and channel conditions. Withina limited time (determined by the application's refresh rate), DynoLoc'stask is to gather as much UWB ranging information (on links) from thenetwork as possible at a master² node (using WiFi for control), so as toaccurately estimate the underlying topology (See FIG. 2). ² One of thenodes doubles up as a master node.

DynoLoc Overview

DynoLoc operates in epochs (rounds), where the locations of all nodesare estimated at the end of each epoch, the duration of which isdetermined by the application's refresh rate (e.g., 0.5-2 Hz). In everyepoch, the following sequence of operations is executed.

I) Topology estimation for ranging Given the underling physical topology(based on connectivity), DynoLoc first determines which of theunderlying critical edges need to be ranged within the limited durationavailable (determined by fresh rate and DynoLoc's ranging protocol). Theedge selection is robust in that it prioritizes edges which contributeto the resulting topology being maximally rigid, while avoiding those,whose ranges could be corrupted by multipath; and adaptive in that itprioritizes edges associated with nodes that have been mobile in therecent past thereby leading to a good localization accuracy.

II) Concurrent ranging The selected edges are then ranged using aconcurrent ranging protocol that amortizes the overhead of ranging froma node across its neighbors, while enabling concurrent ranging innon-interfering neighborhoods, to minimize the overall latency.

III) Robust relative localization After the estimated topology has beenranged, DynoLoc's localization algorithm intelligently identifies andapplies EDM only on sub-graphs of the topology that are rigid andcombine them effectively, to deliver both a robust and accurate relativelocalization.

IV) Absolute localization DynoLoc finally transforms the relativelocalization solution into an absolute one with little additional metainformation (contributed by floor plans or a single reference node),while still delivering the desired refresh rate for the solution.

Estimation of Ranging Topology

One particularly significant innovation embodied in DynoLoc is that itleverages the graph theoretic construct of geometric rigidity toidentify the set of edges that would collectively contribute to theaccurate localization of the topology as a whole while also adaptingitself to track the topology as it evolves with node mobility andchannel conditions. DynoLoc accomplishes this by first characterizingthe links in the physical connectivity topology, followed by leveragingsuch a characterization for adaptive link selection.

A. Characterizing the Connectivity Topology: Every link is characterizedbased on the mobility of its nodes, the multipath nature of its wirelesschannel, as well as its contribution to the topology's rigidity.

LOS vs. NLOS: Every node maintains a list of its neighbors, identifiedby overhearing their transmissions, whose channel impulse response (CIR)is also collected. A node i can thus directly range with any of itsneighbors j, whose link quality (L_(ij)) is estimated from itscorresponding CIR. L_(ij) captures the potential accuracy of ranging onthe link based on the certainty of it being a LOS (direct) or NLOS(indirect) path. This NLOS probability is computed by asp_(NLOS)=(f₁×f₂×f₃×f₄), where f₁=avg. peak count before the detectedfirst path (FP) in the preceding window (See FIG. 8), f₂=ratio ofstd-noise to FP amplitude, f₃=ratio of peak to FP amplitude and f₄=ratioof total received power to FP power. The link quality is the inverse ofthis probability. Initially, during the bootstrapping phase, every nodesequentially broadcasts a beacon packet that is heard by its neighbors.Once the system reaches a steady state, the neighborhood list isimplicitly maintained by all nodes without the need for additionalranging. This helps realize a physical connectivity graph across thenodes, where every edge is a potential candidate for range estimationand is weighted by its quality (i.e. certainty for delivering accurateranges).

Mobility: In addition, every node i also maintains a mobility metric Mthat capture its location uncertainty since its last localization. Thismetric increases as a function of the time-since-localization (TsL) andis computed using the node's acceleration, a_(i) (obtained from itsIMU). In particular, for every IMU read (indexed by k),M(k)←M_(i)(k−1)+v_(i)(k)·Δt, where node velocityv_(i)(k)←v_(i)(k−1)+a_(i)·Δt, and Δt is the elapsed time since the lastIMU read. M_(i) and v_(i) are reset to zero, whenever the node islocalized. When a_(i) is zero (static nodes), we assign an exponentialfunction to M as follows: M_(i)←(e^(TsL)−1). This allows the node to beprioritized for ranging, even if it is static, but sufficient time haselapsed since its last localization.

Geometric Rigidity: Recall that a rigid graph admits a unique relativelocalization solution. Since the connectivity topology of nodes mightnot be a rigid graph in practice, DynoLoc aims to select edges from thisunderlying connectivity that ensures maximal rigidity to the resultingnode topology. It does so by identifying maximal rigid sub-graphs fromthe physical connectivity graph, by leveraging the construct of k-coresub-graphs that are used to ensure graph rigidity. In a k-coresub-graph, every vertex has a degree of at least k. It is known that ak-core sub-graph is rigid in k−1 dimensional space. Hence, for rigidityin 2D, we seek to obtain 3-core sub-graphs³. Note that a 2-coresub-graph will not be rigid in 2D and will admit multiple localizationsolutions. ³ This allows DynoLoc to also be extended for 3Dlocalization, where 4-core sub-graphs will be leveraged instead.

DynoLoc identifies the maximal 3-core sub-graphs by starting with theconnectivity graph and partitioning it into k-core subgraphs for k=1,2and 3 sequentially. It starts with identifying 1-core nodes (one by one)that have a degree of 1 and removes them and their incident edgesiteratively till no more 1-core nodes can be found. Then, it repeats theprocess for 2-core nodes with degree 2. After the removal of 1-core and2-core nodes, we are left with maximal 3-core sub-graphs (as shown inthe example in FIG. 9) that are rigid.

B. Estimating the Ranging Topology: We now describe DynoLoc's algorithmfor edge and hence topology selection that will be used for ranging. Ata high level, DynoLoc aims to devote its ranging resources to links,whose ranges are outdated (due to mobility), followed by those thatcontribute the most to the topology's rigidity, while also avoidingthose with potentially inaccurate ranges (due to NLOS). Specifically, atevery iteration, DynoLoc picks the node (say i) with the highestmobility M (location uncertainty) metric. If i is part of the 3-core,and has more than three edges, then three of its edges with the highestL metric (range accuracy) are selected. Otherwise, its incident edges(≤3) are directly selected. When multiple nodes have the same M metric,the node selection is done based on the kcore metric, with nodesbelonging to a higher core (ties broken with higher node degree)prioritized over those belonging to a lower core. The process repeatsuntil the ranging budget is exhausted by the edges selected for ranging.Initially, when the system is bootstrapped and no information on nodemobility is available, all nodes are assumed to have outdated M metricand edge selection is done primarily based on their contribution torigidity and their LOS nature. The complete DynoLoc method is presentedin Algorithm 1.

Aggregated and Concurrent Ranging

DynoLoc optimizes its ranging protocol using two key mechanisms asfollows.

Aggregated Ranging: UWB employs a two-way ranging (TWR) mechanism,standardized in IEEE 802.15.4, to estimate the distance between a nodepair based on Time of Flight (TOF). Further, every node pair needs to beseparately ranged following a strict TDMA schedule to avoidinterference. This leads to a sequential ranging for the desired nodepairs, consuming significant latency and hindering scalability. TWRinvolves exchanging 4 messages for a single range estimation. DynoLocinstruments an aggregated version of TWR where an initiator node sends abroadcast INIT message that also contains IDs of other nodes that itwants to range with in tandem. The receiving nodes take turnsindividually to send a POLL message, which is followed by a broadcastRESPONSE message from the initiator node. On receiving the RESPONSEmessage, the nodes take turn once again to send a FINAL message. Basedon the timestamps in the above messages the initiator node calculatesthe distance estimates to individual nodes. Thus, our aggregated TWR canrange N nodes in (2N+2) time slots compared to the 4N slots for itssequential counterpart. Given that each transmission slot time can be 10ms, this substantial saving in turn allows for twice as many links to beranged with the same latency budget.

Concurrent Ranging: While the UWB protocol employs TDMA to avoidwireless contention, it does not account for spatial reuse, wherein nodepairs outside each other's interference domain can be operatedsimultaneously. DynoLoc can easily compute such link concurrencyinformation based on the neighborhood information for each node asdiscussed earlier. Hence, a set of initiator nodes and theircorresponding ranging nodes are logically partitioned intonon-interfering groups, with every group containing an initiator node.Each of the initiator nodes executes the aggregated TWR processconcurrently to range relevant nodes in its group. Such concurrentranging, that makes use of spatial reuse, provides a significantincrease in the ranging budget that is beneficial for large nodetopologies.

DynoLoc employs the above ranging protocol to collect ranges for linksin its ranging topology. Such optimizations also result in asignificantly larger ranging budget (within the application's refreshrate) for use by its edge selection component that in turn contributesto a larger localization accuracy.

Robust Relative Localization

DynoLoc employs the latest (epoch) ranges collected from the rangingtopology, along with other ranges from prior epochs that are accurateand not outdated, to determine the relative localization of nodes.Existing solutions require a complete m×m EDM matrix of ranges for a mnode topology to determine the relative localization (embedding) ofnodes. Matrix completion techniques (e.g. OptSpace, SDR) are used tocomplete the missing EDM entries in practice, but deliver pooraccuracies, owing to the lack of (i) rigidity over the entire topology,and (ii) incorporation of geometric structure. DynoLoc innovates in boththese aspects to deliver a robust relative localization solution asfollows.

A. Estimating Missing Ranges: DynoLoc performs EDM completion only onthe rigid sub-graphs (3-core sub-graphs) of the topology, individually.The rigidity of the sub-graph (say, with n nodes) enables a moreaccurate EDM completion of n×n. Further, it leverages the geometrictopology of the nodes to complete the missing EDM entries as follows(Algorithm 2). The algorithm starts by initializing the EDM with themeasured ranges and then fills in the missing ranges through sequentialmultilateration. In the process, it also computes the relative locationsof all the nodes. It maintains two copies of the EDM, one based on theinter-node ranges obtained solely from the computed relative nodelocations, and the other including the actual measured ranges, whereavailable. It then iteratively perturbs or updates the relative locationof each node to minimize the gap between these two EDMs, and furtherupdates the EDM from the subsequent node locations. Thus, the error inthe estimation of missing ranges is minimized.

B. Relative Localization: Having identified the rigid and non-rigidcomponents of the topology, DynoLoc first solves for the relativelocalization of nodes in the rigid sub-graphs individually, followed bythose in the nonrigid components.

Rigid Component: Having completed the EDM for the rigid component,DynoLoc employs the Multi-dimensional Scaling (MDS) solver to find thenode embedding (relative localization) solution. Note that whilesequential multilateration is appropriate for EDM completion, it is notemployed for the eventual localization itself, as the location errortends to increase rapidly for the nodes being mulitlaterated later. Incontrast, with MDS solvers, the order of the multilateration does notmatter—with the multilateration error being part of the objectivefunction, it is equally distributed across all steps of themultilateration process. The MDS problem can be defined as: Given asquared EDM D², find the corresponding relative location matrix X. Inmatrix notation, this is equivalent to solving for B=XX^(T) where X^(T)is the transpose of matrix X. DynoLoc employs the Classical MDS (CMDS)solver, which works as follows:

Given square-distance matrix D², compute Gram matrix B of X as B=−½^(JD)² J, where

$J = {I - {\frac{1}{n}11\; {T.}}}$

Find the Eigenvalue decomposition of B=QAQ^(T),

Finally, compute X=Q sqrt(A) for Eigenvector matrix Q and diagonalEigenvalue matrix A.

Due to the “centering” assumption, each column of solution X sums tozero i.e, the origin of X coincides with the centroid of the nlocations. Note that CMDS minimizes the loss function (also calledstrain) L(X)=∥XX^(T)−B∥=HXX^(T−1/2JD) ² ^(J)∥. In doing so, it updatesthe range entries only for the missing edges but leaves the edges withmeasured ranges untouched. The CMDS can result in local minima, whichare more likely to occur, when the dimension is low (2 as in our case).Hence, DynoLoc cross-validates the locations using heading dataretrieved from the IMUs of nodes and also employs a smoothing filter onthe successive node locations over a small window.

Non-Rigid Component: Having solved for the relative locations of the3-core nodes, DynoLoc now considers the nodes in the non-rigidcomponents, namely the 2core and 1-core nodes. By definition, every2-core node has two edges to the rigid component(s). It employs thesetwo ranges to find the relative location of the 2core node. However,with just two ranges, there are two possible solutions. DynoLoceliminates one of these solutions easily by leveraging the link qualityof edges in the neighborhood of the solution. If the solution has aneighboring rigid node (other than the rigid nodes of its two edges),this will contradict its 2-core status (as another edge to a rigidcomponent exists) and the solution can hence be eliminated. Once the2-core nodes are also solved, the remaining 1-core nodes are determinedusing the angle that the corresponding edge (to the rigid component)makes with the North-South axis of the earth using the IMU data. Thiscomputation is done as part of the absolute localization procedurediscussed next.

Absolute Localization

DynoLoc needs to transform the localization solution from a relativecoordinate system to a target absolute coordinate system. This isachieved by translating, rotating, and flipping the rigid graph derivedin the previous step with the help of IMUs (on the nodes) or floormapinformation.

IMPLEMENTATION AND EVALUATION System Implementation and Prototype

As presently constituted, our DynoLoc system includes a set of UWB tags,interfaced with an embedded computer (e.g., a smartphone or aRaspberry-Pi) and a central controller that orchestrates rangemeasurements and runs the localization algorithms. Both, the controllerand the embedded computer are connected to a local WiFi network forexchanging control information and application data (e.g., sensorreadings, video feeds).

DynoLoc UWB Tag: The tag consists of a Decawave DW1000 ultra-widebandradio (costs ≈10$) that houses an extremely precise picosecond crystal(for TOF calculation), which can achieve a distance resolution as highas 2.2 mm. We interface the DW1000's SPI pins (serial clock, masteroutput, master input and slave select), V_(cc) and GND pins to a lowpower ARM Cortex-M based microcontroller unit STM32 NUCLEOF042K6(costs≈10$), where the latter acts as the SPI master. DynoLoc'soptimized ranging protocol is implemented in about 4000 lines of C codeand runs on the STM32 microcontroller. Additionally, the STM32 sends andreceives specific ranging instructions or estimated ranges through itsserial port from an external host device. While most commerciallyavailable UWB tags cost somewhere between $100-200, DynoLoc tags showthat it is feasible to keep the cost to under 20$ (1020% of the COTStags) without compromising any feature. Our tags can use the UWBpermissible channels spanning from 3-7 GHz (with 500 MHz bandwidth). Inmost of the experiments, we use 3.5 GHz as the center frequency forimproved range.

Tag Host: An embedded computer (e.g., a smartphone) acts as a USB hostfor the DynoLoc Tag. It sends specific ranging instructions to the tag(e.g., range with 5 specific neighboring nodes) and receives measuredranges. The tag host also keeps track of the node's mobility from theinertial sensors as well as the link quality information as obtainedfrom the tag (M and L metrics). The device driver for our tag isimplemented on the Android platform and runs as a background serviceintercepting commands from the controller and passing it on to the tagand vice versa. The tag host also houses a pressure sensor to identifythe vertical elevation, i.e., floor number which is useful in amulti-story deployment scenario.

Controller: The controller is in charge of the overall topologyestimation, gathering ranges from individual tags and running thelocalization engine. Depending on the application, the controller sendsthe information back to the individual nodes, displays them locally in adashboard or offload it to a cloud service for remote visualization ordecision making. The controller logic is implemented in about 1000 linesof Python code.

System Evaluation

DynoLoc is evaluated comprehensively spanning in the-wild deployments tocontrolled experiments in realistic indoor settings (supplemented bysimulations only for larger topologies of over 16 nodes). In thefollowing, we describe our methodology followed by some key performanceresults.

In-the-Wild Deployment

DynoLoc has been deployed and tested in a real firefighter's drill. Atotal of ten firefighters, each carrying a DynoLoc tag individually,enter the test building (2 floors⁴, each ≈50 m×100 m) emulating a severefire incident. The fire chief, stationed outside the building, tracksevery move of the personnel crawling through the dark and smoky passagesthrough DynoLoc's realtime dashboard and instructs them accordinglythrough a walkie-talkie. In one specific incident, a firefighter who waslost and separated from his colleagues issued an SOS call. The chiefknowing his location was able to intervene and immediately assist byredirecting his crew accurately towards the lost firefighter. We learned(from the fire chief) DynoLoc's true value in such challengingscenarios, which are common and often lead to firefighter fatalities. Asnapshot of the drill is shown in FIG. 2. ⁴ A pressure-sensor+UWB basedmechanism, for floor identification at each node locally, was added toDynoLoc for this drill.

Controlled Experiment

Testbed: The testbed includes eight pre-planned navigable routes (markedwith adhesive tapes on the floor) in an indoor area of about 50 m×40 m.The collective length of the routes is ≈500 meters The routes encompassvarious types of indoor areas: open hallways, corridors, meeting rooms,lab spaces and so on, such that we have a fair representation of typicalindoor settings (both LOS and NLOS). In addition to the DynoLoc tag(+smartphone host), each volunteer carries another UWB tag operating ata different frequency (6.5 GHz) for ground truth collection.

Baselines: The ground-truth is collected using a system of denselyplaced, synchronized, static anchor nodes (by multilateration using TDOAinformation), deployed throughout the building floor, which gives alocalization accuracy in the order of 10-20 cm, thereby serving as alower bound on performance for infrastructure-free solutions. We alsoconsider two heuristics that are subsets of DynoLoc, namely H-Agnos andH-Dyn. H-Agnos employs DynoLoc's relative localization component butadopts a naive edge selection approach (ranges every pair of possibleedges through round-robin) that does not account for node dynamics andlink quality. In contrast, H-Dyn's edge selection accounts for nodemobility by ranging on edges incident with the most dynamic nodes, butdisregards the geometric rigidity requirement in its relativelocalization.

Overall Localization Performance: FIG. 11(A) highlights DynoLoc'soverall performance as a function of various factors, namely number ofnodes, fraction of mobile nodes, their velocity and the targetedlocation update rate. DynoLoc scales well for a reasonable number ofnodes (FIG. 11(A)) that is practical in most real life contexts. Even inchallenging scenarios, where all the nodes are mobile (at 1 m/s),DynoLoc provides an average localization error of under 2 meters for a 1Hz update rate, while H-Agnos and H-Dyn incur an error of 6-7 meters and5-6 meters respectively (FIG. 11(B)). Lack of DynoLoc's robust relativelocalization component (included in H-Agnos and H-Dyn), will only leadto further degradation. While H-Agnos accounts for underlying graphrigidity, it is devoid of the notion of node mobility and link-qualityand hence, renders poor accuracy; whereas H-Dyn takes into considerationthe node mobility, but renders poor accuracy due to lack of enforcingrigidity requirement. Also note that, even for a more demanding locationupdate rate of 2 Hz, DynoLoc maintains a sub-meter localization accuracy(FIG. 11c ), even when half the nodes are mobile. DynoLoc's rangingalgorithm being mobility-aware, adaptively expends the available timeresources in collecting the most critical ranges. This allows it todeliver under 2 m accuracy even when 4 times the update rate is desired.Benefits of DynoLoc's Design: DynoLoc's design components arebenchmarked in isolation as follows.

Adaptive Ranging: FIG. 11(A) and FIG. 11(B) clearly show the significantmerits and usefulness of DynoLoc's adaptive ranging (edge selection)mechanism (compared to random selection in Rand). We now explore themerits of its mobility and link quality metrics as part of its topologyestimation.

Mobility Metric: FIG. 13(A) demonstrates that performing the edgeselection (purely based on rigidity constraints) without consideringmobility metric (M) results in a sub-optimal localization accuracy.Additionally, FIG. 12(A) shows the reactive nature of the M metric intracking node mobility through its acceleration.

Link Quality Metric: Similarly, FIG. 13(B) shows the impact of linkquality metric (L) on edge selection. L acts as a classifier for NLOSversus LOS ranges. Particularly in NLOS scenarios, L plays a criticalrole in selecting non-noisy edges, thereby leading to a betterlocalization accuracy. FIG. 13(B) indicates that introducing the Lmetric improves accuracy by 30-40% in two different NLOS scenarios(meeting rooms/office space and lab spaces denoted by NLOS1 and NLOS2respectively).

Robust Relative Localization: FIG. 14(A) shows that DynoLoc'sEDM-completion approach can advantageously bound the range estimation(for missing entries) to within 2 m in most cases. This can beattributed to its approach of targeting the rigid sub-graphsindividually, while using a sequential multilateration approach thatexploits the node geometry for estimating the missing ranges. Its rigidsub-graph based relative localization further contributes to a muchimproved accuracy over existing approaches as seen in FIG. 14(B).

Concurrent Ranging: As seen in FIG. 15(B), DynoLoc's aggregated andconcurrent ranging directly contributes to a larger ranging budget andhence localization accuracy. DynoLoc's tag supports two data rates (low,100 Kbps and high, 6.8 Mbps, FIG. 15(A)). The high data rate results ina lower latency per ranging (2 ms vs 8 ms for low data rate case). Whileit restricts the maximum communication range (about 10 m) and allows formore concurrent ranging, it also requires a higher node density toensure a reasonably connected topology. This is reflected in the resultspresented in FIG. 15(A).

Absolute Localization: Conversion of relative to absolute localizationincurs an additional error of 10-20%, as shown in FIG. 16(A). Asexpected, it increases as the network size grows larger.

FIG. 16(B) shows how the value of heading is tracked over time as theuser moves (walk and sprint). While noisy heading values lead to higheradditional errors, this is still restricted to just 40-60 cm even for auser sprinting at 2 m/s.

End-to-End System Latency: FIG. 17(A) breaks down DynoLoc's overalllatency broadly into three categories. In the table shown in FIG. 17(B),we present some battery life benchmarks for different components of thesystem.

APPLICATIONS

DynoLoc enables a host of location-based applications that benefit fromminimal setup time, preferably without any infrastructure deployment,realtime support.

Multiplayer AR/VR Gaming

Contemporary multiplayer AR/VR gaming systems do not support featuresthat have bearing to players' location in the physical coordinate space.Given the growing demand for ‘location-based entertainment, recentsolutions make use of visual SLAM (using VIO) to localize players withrespect to their individual reference frames. However, for acollaborative multiplayer setting, a global coordinate system isessential. This is accomplished today using either visual markers oranchors that do not offer a smooth multiplayer experience, orexpensive/extensive installation of IR cameras and laser tags that donot offer a cost-effective, on-demand deployment (e.g. consumer homes).

We created a simple Android based multiplayer VR game called DynoSoccerto demonstrate the value that DynoLoc can bring to such gaming systems.DynoSoccer transforms ordinary physical spaces, like a living room thatis not particularly well-lit or textured, into a gaming arena, whereplayers can interact with each other based on their real physicallocations. The game involves a virtual ball that is bounced around bythe players. Each player needs to adjust their position to be in theproximity of the ball in order to ‘kick’ it. However, this requires thesystem to be responsive to the players' and the ball's movement,otherwise resulting in a ‘missed kick’. We show (FIG. 18(A), FIG. 18(B))that even for high ball speeds, DynoLoc results in 50% to 90% less‘missed kicks’ compared to the H-Agnos baseline, increasing the VRexperience significantly. DynoLoc adaptively schedules the relevantranges (compared to round-robin in H-Agnos) in the topology based on themobility of individual players. We plan to fuse VIO with DynoLoc toovercome VIO's challenges in less-favorable visual environments (FIG.1), especially in the multi-player context.

Active Shooter Scenario

We demonstrate in FIG. 19(A) and FIG. 19(B) how DynoLoc can create arealtime geofence for safe evacuation of trapped victims in a chaoticsituation like spotting an active shooter. A person mimicking an activeshooter runs in a specified path. Four volunteers (unaware of theshooter's path) equipped with DynoLoc tags and body camera scout thegeneral area (corridors, hallways etc.). If the shooter is detected inthe video frame, we mark the respective location as unsafe and updatethe geofence (a polygon connecting the unsafe points). We show in FIG.19(A)—right, how localization error can lead to inaccurate geofencingresulting in ‘exposed areas’ or zones that are potentially dangerous butmarked safe. Even a 2 m median localization error can lead to a fewhundred sq. ft. of exposed area, compared to the DynoLoc's limitedexposure.

Algorithm 1 DynoLoc Algorithm 1: Make every node send hello frame 2:Collect and initialize link quality metric L 3: Run core-decompositionbased on L 4: Build initial rigid graph G_(R) 5: Initialize mobiltymetric M 6: while True do 7:  Choose node i with the highest metric M(i)8:  Remove i (with its edges) from G_(R) or V′ 9:  if i has ≥ 3 edgeswith G_(R) then 10:   Select 3 edges for i with max. L 11:   Add i backto G_(R) with selected edges 12:  else 13:   Put i in the excluded nodelist V′ 14:   Select i's edge(s) 15:  end if 16:  Range for the.above-selected edges [§3.3] 17:  Set M(i) ← 0 18:  Update M & L metricsfrom collected data 19:  compute core-decomposition 20:  Remove nodeswhich are not in 3-core, from G_(R) 21:  if Refresh interval elapsedthen 22:   Complete EDM for G_(R) [§3.4.A] 23:   Determine relativelocations [§3.4.B] 24:   Determine & output absolute locations [§3.5]25:  end if 26: end while

Algorithm 2 EDM Completion Algorithm 1: Initialize EDM with incasuredranges 2: Compute node locations using multilatration on the rigid graph3: Fill in the missing values in D using node locations 4: InitializeEDM D_(cur) using node locations 5: while ΔD = |D - D_(cur)| issignificant do 6:  Update location of node i, j for each edge (i, j) inproportion to the ΔD(i, j) 7:  Recompute D_(cur) from node location 8: Fill in missing values in D from node locations 9: end while 10: OutputComplete EDM D

CONCLUSION

We have introduced a problem of latency-bounded infrastructure freelocalization that is central to several dynamic indoor applications.Towards addressing the fundamental tradeoff between latency andlocalization accuracy that arises in these problems, we presented thedesign and practical realization of our DynoLoc system. Through variousdesign innovations, DynoLoc has demonstrated its ability to accuratelytrack a large network of highly mobile entities, without anyinfrastructure support, in real-world firefighters' drill, as well asapplications of multi-player AR gaming, and active shooter tracking

While we have presented this disclosure using some specific examples,those skilled in the art will recognize that our teachings are not solimited. Accordingly, this disclosure should be only limited by thescope of the claims attached hereto.

1. A method for infrastructure-free, radio-frequency (RF) tracking of aplurality of mobile nodes in a dynamic indoor environment comprising:providing each individual one of the plurality of nodes located in theinfrastructure-free, dynamic indoor environment with a radio tag;determining a topology estimate of the plurality of nodes; determiningrelative localizations of the plurality of nodes by generating rigidk-core sub-graphs of the topology estimate; transforming the relativelocalizations into absolute localizations; and outputting indicia of theabsolute locations.
 2. The method of claim 1 wherein the topologyestimate is determined from critical links that contribute to thetopology's localization accuracy and vary spatio-temporally.
 3. Themethod of claim 2 wherein the critical links are determined by fusingthree dimensions of information.
 4. The method of claim 3 wherein thethree dimensions of information include mobility of nodes in the link,certainty of range estimates inferred from channel impulse responsemeasurement, and link contribution to topology geometry in creatingsub-graph producing increased localization accuracy.
 5. The method ofclaim 4 wherein the radio tag provides a wireless communication facilityselected from the group consisting of WiFi, UWB, and mmWave.
 6. Themethod of claim 5 wherein the WiFi communication facility is used forcontrol, UWB is used for ranging.
 7. The method of claim 6 wherein theradio tag further includes an inertial sensor (IMU).
 8. The method ofclaim 7 wherein the relative localizations of the plurality of nodes isdetermined from the rigid k-core sub-graphs by applying an Euclideandistance matrices (EDM) technique to the sub-graphs.
 9. The method ofclaim 8 further comprising determining relative localizations of anyremaining nodes any remaining nodes from their range data, physicalconnectivity and IMU heading data.
 10. The method of claim 9 wherein therelative localizations of the remaining nodes is determined from a floorplan of the dynamic indoor environment, said indoor environment nothaving any beacons.